System identification is the process of estimating parameters of a dynamical system from observed trajectories and input profiles. It is a fundamental component in the control design pipeline as many modern optimal and robust control synthesis methodologies rely on having access to a dynamical system model. Traditionally, system identification  [1]}, [2]}, [3]}, [4]}, [5]}
was limited to asymptotic analysis, i.e., estimators were shown to be consistent under the assumption of infinite data. However, recent results [6]}, [7]}, [8]}, [9]}, [10]} focus on the more challenging task of analyzing the finite sample setting. Theoretically, this type of analysis is more involved and requires tools from high dimensional probability and statistics.
